


import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
						import torch

						from transformers import AutoModelForCausalLM







@require_torch
class 							lowerCamelCase__						(				unittest.TestCase   ):






			"""simple docstring"""






			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"  )
						UpperCamelCase__       =							AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"  ).to(snake_case  )
						UpperCamelCase__       =							-1

						UpperCamelCase__       =							ids_tensor((1, 5)   ,    vocab_size=model.config.vocab_size  ).to(snake_case  )
						UpperCamelCase__       =							model.generate(snake_case   ,    max_new_tokens=10   ,    do_sample=snake_case  )
						UpperCamelCase__       =							tokenizer.decode(greedy_ids[0]  )

						with CaptureStdout() as cs:
									UpperCamelCase__       =							TextStreamer(snake_case  )
									model.generate(snake_case   ,    max_new_tokens=10   ,    do_sample=snake_case   ,    streamer=snake_case  )
						# The greedy text should be printed to stdout, except for the final "\n" in the streamer
						UpperCamelCase__       =							cs.out[:-1]

						self.assertEqual(snake_case   ,    snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"  )
						UpperCamelCase__       =							AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"  ).to(snake_case  )
						UpperCamelCase__       =							-1

						UpperCamelCase__       =							ids_tensor((1, 5)   ,    vocab_size=model.config.vocab_size  ).to(snake_case  )
						UpperCamelCase__       =							model.generate(snake_case   ,    max_new_tokens=10   ,    do_sample=snake_case  )
						UpperCamelCase__       =							tokenizer.decode(greedy_ids[0]  )

						UpperCamelCase__       =							TextIteratorStreamer(snake_case  )
						UpperCamelCase__       =							{"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
						UpperCamelCase__       =							Thread(target=model.generate   ,    kwargs=snake_case  )
						thread.start()
						UpperCamelCase__       =							""
						for new_text in streamer:
									streamer_text += new_text

						self.assertEqual(snake_case   ,    snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"  )
						UpperCamelCase__       =							AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"  ).to(snake_case  )
						UpperCamelCase__       =							-1

						UpperCamelCase__       =							ids_tensor((1, 5)   ,    vocab_size=model.config.vocab_size  ).to(snake_case  )
						UpperCamelCase__       =							model.generate(snake_case   ,    max_new_tokens=10   ,    do_sample=snake_case  )
						UpperCamelCase__       =							greedy_ids[:, input_ids.shape[1] :]
						UpperCamelCase__       =							tokenizer.decode(new_greedy_ids[0]  )

						with CaptureStdout() as cs:
									UpperCamelCase__       =							TextStreamer(snake_case   ,    skip_prompt=snake_case  )
									model.generate(snake_case   ,    max_new_tokens=10   ,    do_sample=snake_case   ,    streamer=snake_case  )
						# The greedy text should be printed to stdout, except for the final "\n" in the streamer
						UpperCamelCase__       =							cs.out[:-1]

						self.assertEqual(snake_case   ,    snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							AutoTokenizer.from_pretrained("distilgpt2"  )
						UpperCamelCase__       =							AutoModelForCausalLM.from_pretrained("distilgpt2"  ).to(snake_case  )
						UpperCamelCase__       =							-1

						UpperCamelCase__       =							torch.ones((1, 5)   ,    device=snake_case  ).long() * model.config.bos_token_id
						with CaptureStdout() as cs:
									UpperCamelCase__       =							TextStreamer(snake_case   ,    skip_special_tokens=snake_case  )
									model.generate(snake_case   ,    max_new_tokens=1   ,    do_sample=snake_case   ,    streamer=snake_case  )

						# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
						# re-tokenized, must only contain one token
						UpperCamelCase__       =							cs.out[:-1]  # Remove the final "\n"
						UpperCamelCase__       =							tokenizer(snake_case   ,    return_tensors="pt"  )
						self.assertEqual(streamer_text_tokenized.input_ids.shape   ,    (1, 1)  )







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"  )
						UpperCamelCase__       =							AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"  ).to(snake_case  )
						UpperCamelCase__       =							-1

						UpperCamelCase__       =							ids_tensor((1, 5)   ,    vocab_size=model.config.vocab_size  ).to(snake_case  )
						UpperCamelCase__       =							TextIteratorStreamer(snake_case   ,    timeout=0.001  )
						UpperCamelCase__       =							{"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
						UpperCamelCase__       =							Thread(target=model.generate   ,    kwargs=snake_case  )
						thread.start()

						# The streamer will timeout after 0.001 seconds, so an exception will be raised
						with self.assertRaises(snake_case  ):
									UpperCamelCase__       =							""
									for new_text in streamer:
												streamer_text += new_text






__UpperCamelCase     		=							2_5_6
# Modulus to hash a string
__UpperCamelCase     		=							1_0_0_0_0_0_3
def 							UpperCamelCase_(					_A :str					,  _A :str	)->		bool:
			UpperCamelCase__       =							len(_A	)
			UpperCamelCase__       =							len(_A	)
			if p_len > t_len:
						return False

			UpperCamelCase__       =							0
			UpperCamelCase__       =							0
			UpperCamelCase__       =							1

			# Calculating the hash of pattern and substring of text
			for i in range(_A	):
						UpperCamelCase__       =							(ord(pattern[i]	) + p_hash * alphabet_size) % modulus
						UpperCamelCase__       =							(ord(text[i]	) + text_hash * alphabet_size) % modulus
						if i == p_len - 1:
									continue
						UpperCamelCase__       =							(modulus_power * alphabet_size) % modulus

			for i in range(0					,  t_len - p_len + 1	):
						if text_hash == p_hash and text[i : i + p_len] == pattern:
									return True
						if i == t_len - p_len:
									continue
						# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
						UpperCamelCase__       =							(
						    (text_hash - ord(text[i]	) * modulus_power) * alphabet_size
						    + ord(text[i + p_len]	)
						) % modulus
			return False
def 							UpperCamelCase_(					)->		None:
			UpperCamelCase__       =							"abc1abc12"
			UpperCamelCase__       =							"alskfjaldsabc1abc1abc12k23adsfabcabc"
			UpperCamelCase__       =							"alskfjaldsk23adsfabcabc"
			assert rabin_karp(_A					,  _A	) and not rabin_karp(_A					,  _A	)

			# Test 2)
			UpperCamelCase__       =							"ABABX"
			UpperCamelCase__       =							"ABABZABABYABABX"
			assert rabin_karp(_A					,  _A	)

			# Test 3)
			UpperCamelCase__       =							"AAAB"
			UpperCamelCase__       =							"ABAAAAAB"
			assert rabin_karp(_A					,  _A	)

			# Test 4)
			UpperCamelCase__       =							"abcdabcy"
			UpperCamelCase__       =							"abcxabcdabxabcdabcdabcy"
			assert rabin_karp(_A					,  _A	)

			# Test 5)
			UpperCamelCase__       =							"Lü"
			UpperCamelCase__       =							"Lüsai"
			assert rabin_karp(_A					,  _A	)
			UpperCamelCase__       =							"Lue"
			assert not rabin_karp(_A					,  _A	)
			print("Success."	)


if __name__ == "__main__":
						test_rabin_karp()


